University of Groningen
Health Self-Management Applications in the Work Environment
Bonvanie, Anne; Broekhuis, Manda; Janssen, Onne; Maeckelberghe, Els; Wortmann, Hans Published in:
Frontiers in Digital Health DOI:
10.3389/fdgth.2020.00009
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Bonvanie, A., Broekhuis, M., Janssen, O., Maeckelberghe, E., & Wortmann, H. (2020). Health Self-Management Applications in the Work Environment: The Effects on Employee Autonomy. Frontiers in Digital Health, 2, [9]. https://doi.org/10.3389/fdgth.2020.00009
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Health Self-Management Applications in
the Work Environment: the Effects on
Employee Autonomy
Anne Bonvanie1, Manda Broekhuis1*, Onne Janssen1, Els Maeckelberghe2, Hans Wortmann1 1University of Groningen, Netherlands, 2University Medical Center Groningen, Netherlands
Submitted to Journal:
Frontiers in Digital Health
Specialty Section:
Connected Health
Article type:
Original Research Article
Manuscript ID: 518927 Received on: 10 Dec 2019 Revised on: 09 Jun 2020
Frontiers website link:
www.frontiersin.org
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest
Author contribution statement
AB designed the experiment and collected the data. AB and OJ analyzed the quantitative data. Interview protocols were drawn by AB, EM, and MB. Analysis of the interview data was done by AB and MB. AB drafted the manuscript under supervision from HW, OJ, EM and MB.
Keywords
Work place health promotion, Sensor technology, wearables, autonomy, health self-management
Abstract
Word count: 185
Organizations increasingly provide Health Self-Management Applications (HSMAs) that provide feedback information to their employees so that they can self-regulate a healthy lifestyle. Building upon Self-Determination Theory, this paper empirically investigates the basic assumption of HSMA use and feedback information, i.e., the provision of perceived autonomy in self-regulating healthy behavior. The two-phase experimental study contained a four-week HSMA intervention with a feedback factor and pretest and posttest measurements of participants’ perceived autonomy. Following the experiment, interviews were conducted with users to gain an in-depth understanding of the findings and in particular the influence of BMI, as a proxy for health condition. Findings reveal that the use of an HSMA does not significantly increase perceived autonomy, and may even reduce it under certain conditions. Providing additional developmental feedback generated more positive results than performance feedback alone. Employees with high BMI sensed a greater loss of autonomy than employees with lower BMI, which is explained by them assigning greater value to general norms, negative emotions when those norms are not met, and increased awareness of their limitations in the environment that hinder their pursuit of health-related behavioral goals.
Contribution to the field
Our research on health self-management in the work environment shows how the autonomy of employees can change by using employer-provided activity trackers. Earlier studies have found negative effects of monitoring tools that were installed for the benefit of the employer, but this study shows that especially employees with a BMI >30 also experience a loss of autonomy when they receive feedback on their health-related behavior from an employer-provided self-management tool - despite the claims that these tools increase peoples autonomy. This loss can be mitigated by using developmental instead of performance feedback, but the manuscript shows that there is a need for caution by employers who want to improve their employees' health.
Funding statement
This study, part of SPRINT@Work, is part-financed by the European Regional Development Fund, the province and municipality of Groningen, and the province of Drenthe [grant number T-3036, 2013].
Ethics statements
Studies involving animal subjects
Generated Statement: No animal studies are presented in this manuscript.
Studies involving human subjects
Generated Statement: The studies involving human participants were reviewed and approved by the Ethics Committee of the Faculty of Economics and Business at the University of Groningen. The patients/participants provided their written informed consent to participate in this study.
Inclusion of identifiable human data
Generated Statement: No potentially identifiable human images or data is presented in this study.
Data availability statement
Generated Statement: The datasets generated for this study are available on request to the corresponding author.
1
Health Self-Management Applications in the Work Environment:
The Effects on Employee Autonomy
Anne Bonvanie1, Manda Broekhuis1*, Onne Janssen2, Els Maeckelberghe3, Hans
1
Wortmann1
2
1 Department of Operations, Faculty of Economics and Business, University of Groningen, 3
Groningen, the Netherlands 4
2 Department of Human Resource Management & Organizational Behaviour, Faculty of 5
Economics and Business, University of Groningen, Groningen, the Netherlands 6
3 Institute for Medical Education, University Medical Center Groningen, University of 7
Groningen, Groningen, the Netherlands 8 * Correspondence: 9 Manda Broekhuis 10 h.broekhuis@rug.nl 11
Keywords: Health self-management, autonomy, wearables, sensor technology, work
12
place health promotion
13
Abstract
14
Organizations increasingly use Health Self-Management Applications (HSMAs) that provide 15
feedback information on health-related behaviors to their employees so that they can self-16
regulate a healthy lifestyle. Building upon Self-Determination Theory, this paper empirically 17
investigates the basic assumption of HSMAs that their self-management feature provides 18
employees with autonomy to self-regulate their health-related behavior. The two-phase 19
experimental study contained a four-week HSMA intervention in a healthcare work 20
environment with a feedback factor (performance vs developmental) and pretest and posttest 21
measurements of participants’ perceived autonomy. Following the experiment, interviews 22
were conducted with users to gain an in-depth understanding of the moderating roles of 23
feedback and BMI (a proxy for health) in the effects of HSMA on perceived autonomy. 24
Findings reveal that the use of an HSMA does not significantly increase perceived autonomy, 25
and may even reduce it under certain conditions. Providing additional developmental 26
feedback generated more positive results than performance feedback alone. Employees with 27
higher BMI perceived a greater loss of autonomy than employees with lower BMI. The 28
reason for this is that higher-BMI employees felt external norms and standards for healthy 29
behavior as more salient and experienced more negative emotions when those norms are not 30
met, thereby making them more aware of their limitations in the pursuit of health goals. 31
32
2
1 Introduction
33
To increase overall productivity and decrease workforce costs, organizations are increasingly 34
embracing workplace health promotion programs as a critical strategy for improving 35
employee health and work outcomes (1,2). These programs tend to focus on individual health 36
factors, such as diet and physical exercise, and represent a broad range of disease prevention 37
and health promotion methods such as health checks (3), gym subscriptions (1), physical 38
activity (e.g., 4–6), and vitality training (2). A common denominator in health promotion 39
programs is an increasing reliance on health self-management applications (HSMAs) that 40
provide individual users with key metrics about their bodily functioning and personal health-41
related behaviors (7,8). For example, wearable activity trackers are used to inform users about 42
the number of steps they take, the number of stairs they climb, and the intensity levels of their 43
physical activities on a daily basis (e.g., 4). 44
A core assumption underlying the use and usefulness of such HSMAs is that their self-45
management feature provides employees with autonomy and control to self-regulate their 46
health-related behavior. Specifically, derived from Self-Determination Theory (SDT) (Ryan 47
and Deci 2000; Ryan and Deci 2006), the notion is that the use of HSMAs promotes a sense 48
of autonomy through which employees become intrinsically and deeply engaged in self-49
regulating their behavior. Critical elements for behavioral change and health improvements 50
are monitoring, goal setting, and action planning (2,7,8,11). However, although a substantial 51
body of research has shown the potential of HSMAs in promoting employee health (4,12), no 52
empirical studies have examined and proven the basic assumption that HSMAs increase 53
employees’ perceptions of autonomy in the self-regulation of their health-related behavior. 54
Indeed, on the contrary, some scholars even suggest a loss of perceived autonomy resulting 55
from self-monitoring technologies (13–17). As such, the literature on HSMAs and employee 56
autonomy is inconclusive with several gaps addressed by this research. 57
First, employers providing HSMAs may impact the relative freedom employees experience in 58
the use of such HSMAs and the self-regulation of their health-related behavior. At first sight, 59
the provision of HSMAs might suggest honorable intentions. Counter-effects however might 60
emerge that affect employees’ sense of autonomy in self-regulating their health-related 61
behavior. The use of worksite HSMAs makes the norms and standards for healthy behavior 62
that are usually latent yet imposed by external entities (e.g., health agencies, employers) 63
salient (18,19). SDT suggests that if this happens, employees may feel that the locus of 64
control over their health-related behavior shifts from internal to external. This potentially 65
decreases their perceived autonomy. Therefore, our first research goal is to investigate the 66
effects of employer-provided HSMAs on employees’ perceptions of autonomy regarding the 67
self-regulation of health-related behavior. 68
Second, HSMAs provide users with feedback information on specific aspects of their bodily 69
functioning and health-related behavior. This information is assumed to facilitate the 70
autonomous self-regulation of healthier behavior. This feedback usually focuses on 71
discrepancies between one’s actual health-related behaviors and standards set for those 72
behaviors, which can be termed as ‘performance feedback’ (20). However, one form of 73
feedback that has hardly been used and examined in the HSMA context is ‘developmental 74
feedback’. Developmental feedback includes information that facilitates recipients to learn, 75
develop, and make adaptive behavioral changes (20). SDT suggests that developmental 76
feedback may boost autonomy and intrinsic motivation for learning and improvement, 77
whereas the evaluative and controlling information provided by performance feedback may 78
inhibit feelings of autonomy (9). Therefore, our second research goal is to investigate the 79
3 potentially moderating role of feedback focus (performance versus developmental) in
80
HSMAs’ effects on perceived autonomy. 81
Third, individual differences, such as initial health condition may influence how employees 82
respond to HSMAs in terms of perceived autonomy in self-regulating their behavior. Previous 83
research showed that employees with poorer self-rated health respond more negatively to 84
health checks with feedback than do healthier respondents (3). Less healthy employees 85
reported experiencing less control over their health-related behavior and feared that health 86
measures imposed by their employer would invade their privacy and interfere with their sense 87
of personal autonomy (3). Therefore, our third research goal is to examine whether an 88
employee’s state of health influences HSMAs’ effects on perceived autonomy. 89
Fourth, health metrics provided by HSMAs such as activity trackers capture daily activities 90
that are carried out both within and beyond the workplace. Further, the standards set for 91
physical activity (e.g., 10,000 steps a day) are usually not limited to the workplace. They are 92
flexible standards for self-regulation of employees’ health-related behavior during both work 93
and private time. Although HSMAs thus appear to blur the lines between work and private 94
time, employees may establish different autonomy feelings in the self-regulation of their 95
health-related behavior in the workplace and at home. Employees may feel that HSMAs 96
provided by their employer invade their private time and thus especially interfere with their 97
sense of autonomy at home. Hence, to address these potentially different autonomy effects of 98
HSMAs across work and private domains, we include measures of both work health 99
autonomy and home health autonomy. Thus, our fourth research goal is to explore whether 100
the effects of HSMAs that focus of feedback and health status are different for employees’ 101
perceptions of health autonomy at work and at home. 102
This study contributes to the HSMA research literature by using insights from SDT and 103
feedback literature to examine the basic assumption underlying the use of HSMAs: that their 104
self-management function promotes employees’ perceptions of autonomy in self-regulating 105
their health-related behavior. Our research shows that the type of feedback (performance 106
versus developmental) that employees obtain from HSMAs, in conjunction with their health 107
condition, affects their perceived autonomy. Also, the effects of feedback and health condition 108
on health autonomy perceptions are different at work and at home. These findings lead to 109
guidelines for the effective use of HSMAs in different settings (work and at home) and for 110
employees with different health conditions. 111
2 Theory and Hypotheses Development
112
An overview of relevant findings from previous studies is provided here, leading to the 113
development of three hypotheses about the effects of HSMAs on perceived autonomy, and 114
how feedback focus and health moderate these effects. We then argue that autonomy should 115
be considered both at work and in private time, leading to an explorative question about the 116
effects of HSMAs for both work health autonomy and home health autonomy. 117
2.1 HSMAs and perceived autonomy in the self-regulation of health-related behavior
118
In the present research, we focus on the use of HSMAs, specifically the Fitbit One activity 119
tracker. HSMAs provide users with feedback information on bodily functioning and health-120
relevant behaviors such as heart rate, steps taken, stairs climbed, and intensity of physical 121
activity. Such devices are used in various domains, ranging from clinical settings for disease 122
management (18) to occupational settings for disease prevention and health promotion (2,6). 123
4 Reviews evaluating the effectiveness of different methods for promoting physical activity 124
reveal that activity trackers can be very effective in increasing the number of steps 125
participants take (6,21). This increase in activity however does not by definition imply an 126
increase in perceived autonomy of users. On the contrary, Owens and Cribb (19) argue that 127
HSMAs do not inherently increase autonomy, and are even likely to decrease it, because 128
externally imposed norms and values are likely to undermine genuinely autonomous 129
deliberation by users. To date, research has not systematically and empirically examined how 130
HSMAs influence employees’ perceived autonomy in self-regulating their health-related 131
behavior. Therefore, we aim to address this gap in the research literature. 132
SDT(9,10) is seen as a promising framework for the study of autonomy in the self-regulation 133
of health-related behavior. This theory contends that the quality of human motivation for 134
regulating behavior varies along a continuum from autonomous motivation to externally 135
controlled motivation. Individuals are autonomously motivated if they experience an internal 136
locus of causality and self-determination in the self-regulation of goal pursuits. In contrast, 137
controlled motivation is present when individuals experience an external locus of causality in 138
goal pursuits, which occurs when their goal-directed behavior is controlled and regulated by 139
externally imposed norms, standards, or sanctions. Research has shown that an increase in 140
perceived autonomy promotes effective cognitive, affective, and behavioral self-regulation of 141
health-related behavior (11,22–26). 142
The first goal of this study is to examine the effect of a workplace HSMA intervention on 143
employees’ perceptions of autonomy in self-regulating their health-related behavior. 144
Specifically, using an experimental field study in a company in the healthcare industry, we 145
examine whether the use of an activity tracker (Fitbit One) provided by the employer 146
increases or decreases the sense of autonomy that employees experience in regulating their 147
health-related behavior. Here, we build two competing hypotheses regarding the effects of 148
HSMAs on autonomy. 149
Using HSMAs enables employees to self-monitor their personal fitness metrics, and to 150
become aware of the extent of their physical activity. This self-awareness facilitates users to 151
reflect on their personal health situation and then to focus on goal setting, action planning, and 152
actual engagement in physical activities to improve their health (21). This reliance on self-153
regulation makes employees responsible for their own health and enables them to 154
independently self-manage their health-related behavior. SDT argues that self-responsibility 155
and self-direction facilitate a more self-determined form of motivational regulation of 156
behavior (27). Therefore, the first part of our competing hypothesis predicts that HSMAs have 157
a positive effect on employees’ perceptions of autonomy in self-regulating their health-related 158
behavior (Hypothesis 1a). 159
However, even though HSMAs aim to facilitate autonomy in self-regulating health-related 160
behavior, HSMAs might also interfere with the development of autonomous self-regulation. 161
First, employer-provided HSMAs have been found not to be value-free (17), and may impose 162
norms and standards, or expectations, for health-related behaviors. Specifically, by expecting 163
employees to use HSMAs such as activity trackers, employers not only highlight health 164
values but also impose guidelines, norms, or standards for physical activity (e.g., 10,000 steps 165
a day), even if these are not explicit. As a result, employees may feel that the HSMAs 166
interfere with their personal autonomy and free choice to behave in ways that the employer 167
sees as undesirable, unfit, and unhealthy (18). They may perceive the use of HSMAs as a 168
form of surveillance and control, leaving them no real choice, even if the employee is the only 169
person with access to the data. 170
5 Second, HSMAs, such as activity trackers, focus on self-regulating health-related behaviors 171
not only in the workplace but also in private life. For example, goals set for physical activity 172
(such as 10,000 steps a day) are formulated as fluid goals that transgress and blur the border 173
between work and private spheres (16,28). With this continuous exposure to HSMAs, both in 174
work and in private time, employees may experience the HSMAs as invading their privacy 175
and decreasing their personal autonomy (16). Accordingly, based on these two arguments that 176
HSMAs may constrain free-choice behavior and interfere with privacy, the second part of our 177
competing hypothesis argues that HSMAs have a negative effect on employees’ perceptions 178
of autonomy in self-regulating their health-related behavior (Hypothesis 1b). 179
2.2 The moderating role of focus of feedback
180
The essence of HSMAs is to provide feedback information on health-related behavior so that 181
users can adjust their behavior to meet desired standards. HSMAs usually deliver 182
performance-oriented feedback, which can be defined as information concerning 183
discrepancies between one’s actual performance (e.g., 6000 steps per day) and the 184
performance standard (e.g., 10,000 steps per day)(29). Such information focuses on past 185
performance, while its valence is critical in determining one’s current and future behavior in 186
regulating progress towards a standard (20). Another type of feedback is developmental 187
feedback, defined as helpful or valuable information that enables the recipient to learn, 188
develop, and make improvements (30). As such, this type of feedback focuses on the future 189
rather than the past, with the feedback providing the recipient with developmental information 190
that is helpful in improving certain performance dimensions (20). 191
We offer two arguments for why focus of feedback could moderate the effects of HSMAs on 192
autonomy. First, using only performance feedback may tend to increase the salience of the 193
potentially inhibitory effects of HSMAs on autonomy. This is because performance feedback 194
highlights norms and standards for healthy behavior that are construed and imposed by 195
external entities (i.e., employer or health agencies) rather than freely determined by the 196
feedback recipients themselves (29). Due to this external imposition of health norms and 197
standards, employees may perceive performance feedback as evaluative and controlling 198
information intended to subtly force them to adapt their health-related behavior in line with 199
the externally imposed standards. Consequently, HSMAs that only use performance feedback 200
are likely to induce an external rather than an internal locus of causality in employees for 201
regulating their health-related behavior. 202
Second, in contrast, the use of developmental feedback may tend to boost the salience of the 203
potentially supportive effects of HSMAs on autonomy. This is because developmental 204
feedback is informational in nature and fosters an orientation toward learning and 205
development (20). Specifically, developmental feedback provides meaningful information 206
that enables employees to learn why the recommended health-oriented behavior is important. 207
Moreover, developmental feedback offers employees alternative options and ways to achieve 208
behavioral change and health improvements. Since these options provide choice and self-209
direction, developmental feedback enables employees to experience themselves as 210
autonomous initiators and regulators of health promotion actions (11,22). Accordingly, we 211
hypothesize that the focus of the feedback moderates the effects of HSMAs on employees’ 212
perceptions of autonomy in self-regulating their health-related behavior, such that the effects 213
are more positive when employees receive developmental feedback in addition to mere 214
performance feedback (Hypothesis 2). 215
2.3 The moderating role of health
216
6 Employees differ in their health status, and these individual differences seem to influence how 217
they respond to workplace health promotion programs. Recent research shows that less 218
healthy employees experience more difficulties in adhering to healthy lifestyle behaviors 219
recommended by guidelines (31,32). They feel that workplace health promotion programs 220
invade their privacy and go against their personal autonomy (3). Given this finding, we 221
examine how differences in individual health conditions moderate the effects of HSMAs on 222
autonomy. Here, we use body mass index (BMI) as a holistic measure of health (33). We use 223
BMI as a proxy of health because of its high predictive validity across many health outcomes 224
and widespread use in population and medical research, and because it is a convenient and 225
simple measure of health that can be self-reported by individuals without requiring inputs 226
from medical authorities (33). 227
We discuss two reasons why BMI might moderate the effects of HSMAs on employees’ 228
perceptions of autonomy in self-regulating their health-related behavior. First, HSMAs may 229
encourage weight-based stereotypes that overweight individuals are lazy and unattractive, and 230
lack self-discipline and willpower, thus assigning responsibility and blame to overweight 231
individuals with unhealthy lifestyles (32,34). As a consequence, workplace health promotion 232
measures may be seen as a violation of privacy and a painful interference with personal 233
autonomy to live life on one’s own terms (34). Moreover, employees with a high BMI may 234
see the use of HSMAs as an attempt by their employer to subtly press them to take action to 235
reduce their weight, thereby harming their sense of self-determination and autonomy. In 236
contrast, as thinness is seen as the healthy ideal (33), employees with a healthy BMI will not 237
feel stigmatized when an HSMA provides feedback information about suboptimal health-238
related behaviors. Not feeling stigmatized, and helped by the feedback from the HSMA, they 239
are more prepared, than high BMI employees, to reduce the suboptimal behaviors identified 240
and stay healthy. 241
Second, employees with high BMI often need to make more drastic lifestyle changes than 242
employees with healthy BMI to meet the standards for healthy physical activity and weight 243
that are made salient by HSMAs. Such changes are far more difficult to achieve for 244
overweight individuals (31), leaving them with a much greater likelihood of failing to adhere 245
to the recommended guidelines (32). Failure adds to the stigmatization and stereotyping of 246
overweight individuals, increasing their vulnerability to psychological distress and the risk of 247
backsliding into unhealthy lifestyle behaviors (32). Consequently, employees with high BMIs 248
may feel they are less able to regulate and change their lifestyle behaviors to meet the HSMA 249
standards and recommended guidelines. This decreases their sense of autonomy and self-250
regulation. In contrast, healthy employees with an optimal BMI often need to make far less 251
difficult lifestyle changes to meet the recommended guidelines and standards. As such, their 252
healthy BMI facilitates self-efficacy and self-control in regulating health-related behavior, 253
which reinforces perceptions of self-direction and autonomy. Based on the above reasoning, 254
we hypothesize that BMI moderates the effects of HSMAs on employees’ perceptions of 255
autonomy in self-regulating their health-related behavior, such that the effects are more 256
strongly negative (or less strongly positive) for employees with higher BMIs than for 257
employees with lower BMIs (Hypothesis 3). 258
2.4 Health autonomy at work and at home
259
HSMAs such as activity trackers provide users with physical activity metrics that are usually 260
measured on a daily basis and capture activities carried out within and beyond the workplace. 261
Further, the standards set for physical activity (e.g., 10,000 steps a day) are not specified 262
exclusively for the workplace but are fluid goals for health-relevant behaviors in both work 263
7 and private lives. Thus, besides their influence on autonomy and control of health-related 264
behavior in the workplace, HSMAs may also affect the sense of autonomy that employees 265
experience in regulating their health-related behavior at home. On the one hand, the fluidity of 266
HSMAs may enhance perceived autonomy in both domains. The pursuit of health-related 267
goals (e.g., 30 minutes of moderate intensity exercise each day) is not limited to the work 268
domain but continues into private time. This fluidity in goal pursuits in work and private 269
domains is comparable with tele-working that may facilitate flexibility to reach both work and 270
family goals in the same time frame (35). However, on the other hand, employees may 271
experience the continuous exposure to the HSMA’s demands as an interference with their 272
determination in personal life. This might decrease their perceived autonomy in self-273
regulating their health-related behavior. Accordingly, we examine the potentially different 274
effects of HSMAs on perceived autonomy at work and at home. We do so by including 275
measures of both Work Health Autonomy (WHA), defined as perceived autonomy to regulate 276
health-related behavior during working hours, and Home Health Autonomy (HHA), referring 277
to perceived autonomy to regulate health-related behavior during private time. Previous 278
research on autonomy in the workplace does not lend itself to deriving theoretical 279
argumentation for different HSMA effects on these two distinct types of health autonomy. 280
Therefore, the distinct measures of work and home health autonomy are studied in an 281
exploratory fashion, rather than attempting to develop and test theory-driven hypotheses. 282
Thus, our exploratory research question is whether HSMAs, feedback focus, and BMI have 283
different effects on employees’ perceptions of work health autonomy and home health 284
autonomy. 285
3 Methods
286
3.1 Design, sample, and procedure
287
To examine the effects of employer-provided HSMAs on employees’ perception of autonomy 288
in the self-regulation of their health-related behavior, we executed a pretest-posttest 289
randomized two-phase field experiment study in a company in the Netherlands. The study 290
included a four-week HSMA intervention with a feedback factor (performance versus 291
development feedback) and pretest (T1) and posttest (T2) measurements of participants’ 292
perceptions of autonomy. After the experiment period, a series of interviews was conducted 293
with employees with varying BMIs. 294
Setting: The company involved is a medium-sized hospital that had started an organization-295
wide workplace health promotion program to facilitate the health, well-being, and work-life 296
balance of its employees. The company employs a variety of workers such as nursing and 297
technical staff , specialists and support staff , and office workers with varying levels of 298
mental and physical activities. As one-size-fits-all advices for health promotion may not 299
match such a heterogenous workforce, the hospital management team decided to provide 300
employees with measures through which employees could self-regulate their own unique 301
health behavior including an activity tracker (Fitbit One). However, before implementing this 302
activity tracker throughout the hospital, the management team wanted to investigate its effects 303
and asked us to conduct an experimental field study. The experimental protocol for the study 304
was approved by the designated research ethics committee and sent to the ethics committee of 305
the healthcare institute for information purposes. 306
Participants: Participants were recruited by sending e-mails and a newsletter to all employees 307
in which they were informed about the experiment and offered the opportunity to participate. 308
Employees who were interested in the use of HSMAs are likely to be overrepresented in the 309
8 sample. However, given that workplace health promotion programs usually rely on
310
voluntarily participation and that participation rates vary from 10% to 64% (with an average 311
of 33%) (36), we think that the sample in the present experimental field study is 312
representative for the total population of employees that voluntary participate in health 313
promotion programs. In total, 166 employees responded out of 1525 potential participants 314
(11%). Of these, two were unable to participate due to lengthy absences during the 315
experiment period. Of the remaining 164 employees, 30 were assigned to a pilot group that 316
was used to test and improve the methodological, technical, and logistical features of our 317
experiment. Eleven participants were interviewed after finishing the experiment. All 318
participants in both the pilot group and the main experiment gave an informed consent. 319
Pilot: During the pilot, the technical feasibilities of the HSMAs and data-logging system were 320
tested and evaluated, and modifications were made where necessary. Moreover, small 321
alterations were made to improve the wording of some questionnaire items, and additional 322
information was added to the information sheet for new participants, especially about the use 323
of participants’ research accounts for data gathering and preventing them from linking the 324
HSMA to their own smartphone. 325
Main experiment: The 134 participants that were not involved in the pilot were randomly 326
assigned to either the performance feedback condition (PFC; N= 68) or the developmental 327
feedback condition (DFC; N= 66). These 134 participants were invited by email to complete 328
an online questionnaire at the pretest measurement point, and 122 completed the questionnaire 329
(NPFC = 62, NDFC = 60). The 122 participants that completed this pretest were provided with 330
an HSMA. Of these 122, 20 dropped out, either because they did not use their HSMA or 331
because they did not complete the post-experiment questionnaire distributed after the four-332
week intervention period (see Figure 1 for detailed participant flow chart). Consequently, the 333
final sample included 102 participants (NPFC = 50, NDFC = 52). The retention rate of the 334
participants therefore is 76,1%, which is higher than most e-health interventions in the 335
workplace showing high to very high attrition rates (37), with only 20% of studies reaching a 336
retention rate of 75% or more (38). Of the remaining participants, 84% were female. The 337
participants average age was 46 (SDage = 10), and their average employment duration was 338
11.9 years (SDemployment = 10.4). Most participants (64%) had a higher education or university 339
degree, while 25% had a vocational degree, and 11% had less formal education. The spread of 340
employees across the job spectrum was considered satisfactory, including both administrative 341
and medical personnel, ranging from management and medical specialists to nursing, 342
administrative, and technical staff. 343
!! Insert Figure 1: Participation flow chart here !! 344
3.2 HSMA intervention and manipulation of feedback focus
345
Procedure: After completing the pre-test questionnaire, the participants were informed about 346
the HSMA intervention following a standardized procedure. This involved a letter stating the 347
goal of the study, the duration of the experiment (4 weeks), the expectations of the 348
participants (to wear a Fitbit for the four weeks, complete a post-test questionnaire, and 349
participate in a focus group or interview if asked to), the expected time-investment, and 350
information on data confidentiality. Participants were not expected to use any smartphone or 351
other applications connected to the device, and all data were collected and stored in accounts 352
used only for research purposes. All participants were made aware that their employer did not 353
have access to the data obtained using the activity tracker. The participants then received an 354
9 activity tracker that measured their number of steps taken, stairs climbed, and minutes of 355
light, moderate, and heavy activities during the day. 356
Manipulation of feedback focus: The screen of the activity tracker provided the participants 357
with their personal activity metrics on a daily basis. In addition, they received an email once a 358
week reporting their physical activity metrics in which the focus of the feedback was 359
manipulated. Specifically, participants under the performance feedback condition received 360
only performance feedback information showing factual metrics as assessed by the activity 361
tracker for each of the past 7 days (e.g., October 18: 8000 steps, 14 stairs, 77 minutes light 362
activity, 20 minutes moderate activity, and an estimated calorie use of say 2200 kCal) and the 363
general norms for these measures (10,000 steps a day and a calorie intake of 2000 kCal for 364
women, 2500 kCal for men). Participants under the developmental feedback condition in 365
addition received development feedback, giving advice on how work-related activities could 366
be altered in order to encourage a healthy behavior pattern and lifestyle (see Appendix 1 for 367
feedback examples). These developmental feedback mails included information on the 368
intensity of daily activities, ways to increase their daily activity, tips and tricks to adjust and 369
sustain exercise patterns, and information on food and nutrition. This feedback was based on 370
advice from the Netherlands Nutrition Centre, the National Institute of Public Health and the 371
Environment, and the Knowledge Centre for Sport & Physical Activity. The developmental 372
feedback information in the e-mails was refreshed weekly, and built upon the information 373
given in the previous week(s). 374
3.3 Measures
375
Autonomy. We adapted the three items of the Autonomy scale of the Job Diagnostic Survey 376
(39) developed by Hackman and Oldham (40) to assess participants’ perceptions of work 377
health autonomy (WHA) and home health autonomy (HHA). We pretested the suitability of 378
the individual items of this adapted autonomy scale and solved small wording issues that led 379
to confusion with some of the participants. For WHA, one item from the initial Autonomy 380
scale was applied to capture autonomy experiences for both the work as a whole and 381
individual tasks, resulting in 4 items for WHA. Two example items are “I can independently 382
decide how to take my health into account when executing my job” (WHA) and “In my 383
private time, I’m free to decide whether I want to do something about my health and health-384
related behavior” (HHA). We used a five-point Likert response scale ranging from 1 (strongly 385
disagree) to 5 (strongly agree). See Table 1 for items and statistics of an exploratory factor 386
analysis testing the discriminant validity of the two autonomy scales. 387
BMI. Participants reported their body weight and height. These self-reported values were used 388
to calculate their Body Mass Index. 389
Control variables. We included the demographic variables of gender, age, organizational 390
tenure, education, and previous experience with activity trackers (yes vs. no) as control 391
variables as these variables could potentially influence participants’ perceptions of work and 392
home health autonomy. 393
3.4 Statistical analyses
394
To examine the impact of the HSMA intervention (activity tracker) on perceptions of 395
autonomy in self-regulating health-related behavior during work and personal time, paired-396
sample t tests were conducted to test differences between pretest (T1) and posttest (T2) 397
autonomy (Hypotheses 1a and 1b). This was done for WHA and HHA separately to 398
investigate our explorative question. Having formulated competing hypotheses on the 399
10 direction of the autonomy effects of HSMA, we used two-tailed tests using a significance 400
level of .05. Further, multiple regression analyses were conducted to test the hypothesized 401
effects of feedback focus and BMI on T2 autonomy in self-regulation of health-related 402
behavior, thereby including T1 autonomy as a covariate (Hypotheses 2 and 3). Specifically, 403
the regression analyses consisted of two steps. The first step, in addition to the covariate of T1 404
autonomy, included dummies for feedback focus (performance = 0, developmental = 1) and 405
BMI to test their effects on T2 autonomy. The second step included the cross-product term of 406
feedback focus and BMI to explore their possible interaction effects on T2 autonomy. Our 407
hypotheses had specified the direction of the moderating impacts of feedback focus and BMI 408
on the autonomy effects of HSMA. Therefore, we used one-tailed tests with a significance 409
level of .05. To facilitate interpretation and minimize multi-collinearity problems when testing 410
interaction effects, we used cross-product terms of standardized predictors. Again, we ran 411
separate regression analyses for work (WHA) and home health autonomy (HHA) to examine 412
our explorative question. 413
3.5 Second stage of the study: interviews
414
To explore the mechanisms underlying the moderating effects of feedback and BMI that we 415
identified (see Results section), additional qualitative data were gathered after completing the 416
experimental period. The first author conducted interviews with 11 participants who were 417
spread across the BMI spectrum. Two participants had BMI values lower than 20, two had 418
BMI values between 20 – 25, three had BMI values between 25 – 30, two had BMI values 419
between 30 and 35, and two had BMI values above 35. Interview requests were sent randomly 420
to four participants in each BMI-category, and upon positive response an interview was 421
scheduled. Seven interviewees were in the performance feedback condition, four interviewees 422
were in the developmental feedback condition. The interviews were semi-structured, and 423
protocol questions were focused on how interviewees had experienced and responded to the 424
HSMA feedback in regulating their health-related behavior in the workplace and in private 425
time. The duration of the interviews was 25-45 minutes, and all the interviews were 426
conducted during or immediately after working hours, unless the interviewee requested 427
otherwise. All interviews were taped and transcribed, and a common codebook of 35 codes 428
was generated by having two authors separately and iteratively code one interview, and then 429
compare and align their codes. This codebook was validated by analyzing two further 430
interviews that were coded using this codebook by both these authors, resulting in an 431
interrater reliability (Holsti’s coefficient) of .78 (41). After this validation check, the 432
codebook was used by the first author to code all 11 interviews. Following the coding of the 433
interviews, network diagrams of co-occurring and consecutive codes were made for each 434
interview separately and checked for consistency in interpretation by another author. The 435
individual diagrams were clustered into sub-groups based on BMI score and feedback type to 436
trace any patterns within and between sub-groups of interviewees. This allowed us to further 437
analyze and clarify the roles of both BMI and feedback focus in the autonomy effects of 438
HSMAs. 439
4 Results
440
4.1 Exploratory factor Analyses
441
In order to get some evidence for the discriminant validity of the autonomy scales that were 442
created by adapting the Autonomy scale of the Job Diagnostic Survey, the items of the WHA 443
(4 items) and HHA (3 items) scales were factor analyzed using principal components 444
extraction and oblique rotation. As can be seen in Table 1, two factors emerged with 445
11 eigenvalues greater than 1, accounting for 70,35 percent of the variance. Each item “loaded” 446
on its appropriate factor, with primary loadings exceeding 0,701 and cross-loadings lower 447
than 0,094. The correlation between the two factors was insignificant. 448
!! Insert Table 1: Results of Factor Analysis for WHA and HHA here !!
449
4.2 Equivalence of experimental feedback groups
450
Prior to hypothesis testing, we conducted a one-way analysis of variance (ANOVA) to check 451
the pretest equivalence of the variables across the two experimental feedback groups. That is, 452
we tested whether the participants in the performance feedback group systematically differed 453
from the participants in the developmental performance group with respect to their scores on 454
the demographics of gender, age, organizational tenure, experience with HSMAs, education 455
level, and BMI, and on the study variables of work health autonomy and home health 456
autonomy at the pretest measurement point (T1). As can be seen in Table 2, the ANOVA 457
results did not indicate significant differences for any of the variables, showing pretest 458
equivalence of the variables across the two feedback groups. 459
!! Insert Table 2: ANOVA results here !!
460
4.3 Descriptive statistics
461
Table 3 presents means, standard deviations, and correlations for all the variables included. 462
The correlations indicate that none of the control variables are significantly related to the 463
autonomy variables, leading us to exclude them from our analyses to avoid biased parameter 464
estimates (42). 465
!! Insert Table 3: Means, standard deviations, and zero-order Pearson correlations for
466
variables here !!
467
4.4 Hypothesis Testing
468
4.4.1 Pretest-posttest differences in autonomy. 469
To test Hypothesis 1, we examined whether the use of the HSMA activity tracker influenced 470
employees’ perceptions of WHA and HHA. Specifically, we conducted paired-sample t tests 471
to determine if there were significant differences between pretest and posttest means for the 472
respective autonomy variables. Table 4 reports the pretest-posttest means, standard deviations, 473
and t-values for both WHA and HHA. These are visualized in Figures 2 and 3. The difference 474
between the pretest and posttest means is not statistically significant for WHA, whereas it is 475
significant for HHA (t = -3.184, p < .01) indicating that the use of HSMAs decreased 476
employees’ perceptions of autonomy in regulating their health-related behavior in their 477
private time. Thus, based on these results, Hypothesis 1a, predicting a positive effect of 478
HSMAs on employees’ perceptions of autonomy in self-regulating their health-related 479
behavior, was rejected, whereas Hypothesis 1b, predicting a negative effect of HSMAs on 480
perceived autonomy, was confirmed for HHA but not for WHA. 481
!! Insert Table 4: Results of paired-sample t tests here !!
482
!! Insert Figure 2: Results of paired sample t tests WHA and 3: Results of paired sample
483
t tests HHA here !!
484
4.4.2 Effects of feedback focus and BMI.
485
12 Regression analyses, separately conducted for WHA and HHA at T2, showed that the
486
feedback focus (performance versus developmental) had a marginally significant and positive 487
effect on T2 WHA (b = .10, t=1.44, p < .10, one-tailed test). In line with Hypothesis 2, this 488
finding indicates that the effect of HSMAs on WHA was more strongly positive when 489
employees received developmental feedback than when they received only performance 490
feedback. Feedback focus had no significant effect on T2 HHA (b = .03, t=.44, p >.05, one-491
tailed test), which contradicts Hypothesis 2. Table 4 reports these regression results under 492
Model 1. 493
!! Insert Table 4 here !!
494
Furthermore, as can be seen in Table 4 under Model 1, BMI had significant negative effects 495
on both T2 WHA (b = -.12, t=-1.73, p < .05, one-tailed test) and T2 HHA (b = -.17, t=-2.16, p 496
< .05, one-tailed test). These results indicate that the effects of the HSMAs on both WHA and 497
HHA were more strongly negative for employees with high BMI levels than for employees 498
with low BMI levels, a finding fully in line with Hypothesis 3. 499
In addition, for exploratory reasons, we tested for interaction effects between feedback focus 500
and BMI (see Table 4, Model 2). The interaction effect was significantly positive for WHA (b 501
= .12, t=1.75, p < .05, one-tailed test) and significantly negative for HHA (b = -.21, t=-3.00, p 502
< .01, one-tailed test). Additional simple slope tests (see Figure 4) indicate that BMI was 503
significantly and negatively associated with T2 WHA (b = -.23, t=-2.47, p < .05) for 504
participants who had received only performance feedback, but that BMI was unrelated to T2 505
WHA (b = .02, t=.18, ns) for employees who had also received developmental feedback. 506
Thus, the effects of the HSMAs on WHA were more strongly negative for employees with 507
high BMI levels who received performance feedback, whereas BMI did not moderate the 508
effects of HSMAs on WHA when employees received only developmental feedback. 509
!! Insert Figure 4: Pattern of interaction effect of BMI and feedback focus on T2 work health
510
autonomy here !!
511
In contrast, the interaction plot displayed in Figure 5 shows that BMI was unrelated to T2 512
HHA (b = .02, t= .21, ns) for participants who received only performance feedback, whereas 513
BMI was significantly and negatively related to T2 HHA (b =-.41, t=-3.73, p<.001) for 514
employees who received additional developmental feedback. As Figure 2 shows, with 515
developmental feedback alone, the highest levels of HHA are to be found in low BMI 516
employees, with the level of HHA decreasing strongly at higher BMI levels. 517
!! Insert Figure 5: Pattern of interaction effect of BMI and feedback focus on T2 home health
518
autonomy here!!
519
4.5 Supplementary analysis of additional qualitative data
520
The qualitative interview research focused on understanding two of the main findings from 521
the quantitative study: 522
1. Performance feedback group: the use of HSMAs resulted in a greater reduction in 523
work health autonomy for employees with a higher BMI (see Figure 4) 524
2. Developmental feedback group: the use of HSMAs resulted in a greater reduction in 525
home health autonomy for employees with a higher BMI (see Figure 5) 526
In order to identify the underlying mechanisms that cause these differences in perceptions of 527
autonomy between employees with low and high BMIs, we asked the interviewees about their 528
13 experienced autonomy both at work and at home, and the impact of the Fitbit and the received 529
feedback on this autonomy. In this section, we present the effects that we uncovered and 530
illustrate these with quotes from the interviewees. 531
4.5.1 BMI, Performance Feedback, and Work Health Autonomy
532
Employees with a high BMI experienced the standard norms highlighted in the performance 533
feedback as very challenging and indicated that the use of the Fitbit made these norms more 534
salient, whereas employees with a low BMI tended to interpret the performance feedback 535
more loosely, and give it a positive spin: 536
I discussed it with a colleague who also participated in the Fitbit experiment, and it 537
really depends on what patient rooms you are assigned to. Some are at the front of the 538
department, and then you have to walk a lot more compared to rooms close to the 539
counter. […] And then I thought, I only make this number of steps, I really have to 540
walk some extra kilometers. (Q1: Medical personnel, performance feedback, high 541
BMI) 542
Yes, I often don’t make the 10,000 steps, but that number is also something that was 543
once made up. (Q2: Medical personnel, performance feedback, low BMI). 544
Further, employees with a high BMI commented that the performance feedback made them 545
very aware of the fact that they could not achieve the 10,000 steps norm. They found this very 546
confronting, leading them to express more negative emotions and feelings about the 547
performance feedback they received. As such, high BMI employees seem to experience the 548
performance feedback as more of a burden: 549
Well, I thought I was quite active, and when I started [the experiment] I walked quite 550
a lot […] But it was quite disappointing, how little you move or exercise at work. (Q3: 551
Medical personnel, performance feedback, high BMI) 552
I now [after the experiment, AB] have an app that registers everything. […] and then I 553
think, ooh, did I only walk so little? That is not a lot for a day like that! And then I get 554
embarrassed about it, this isn’t good, especially because I worked the entire day. (Q4: 555
Administrative personnel, performance feedback, high BMI) 556
Third, employees with a high BMI relatively more often experienced obstacles to self-557
regulating and intensifying activity in the work situation. That is, they tended to see more 558
obstacles such as scheduling or work pressure issues. Moreover, employees with a high BMI 559
felt less need to compensate for this lack of opportunity to self-regulate at work in the home 560
situation: 561
[…] No, because that is impossible. We don’t have breaks, and no lunchbreak, so we 562
pretty much work for eight hours straight. So, we can’t go for a walk outside or 563
something. (Q5: Administrative personnel, performance feedback, high BMI) 564
We discussed it [among colleagues], that it would be great to have the opportunity to 565
go for a walk during lunch, but now we only have time to quickly finish eating and 566
then our break is over. (Q6: Medical personnel, performance feedback, high BMI) 567
Because I have less spare time, I don’t achieve it [the 10,000 steps]. And, as I said, 568
sometimes [after work] I’m too tired, and then I start thinking that I would have to 569
14 walk, no, I can’t always make that. Time wise, or energy wise. (Q7: Medical
570
personnel, performance feedback, high BMI) 571
However, employees with a low BMI experienced more self-regulating options and less 572
obstacles to move at work, and seemed to use the feedback from the HSMA to adapt their 573
behavior in the work environment: 574
I started taking the stairs. […] Otherwise I didn’t really exercise more, but I took the 575
stairs more often, because we’re [at work] on the third floor and therefore climb three 576
flights of stairs. (Q8: Medical personnel, performance feedback, low BMI) 577
Yes, I really think a thing like that [HSMA] helps to exercise more. Because I have 578
sometimes caught myself thinking, darn, I’m taking the elevator [at work] when I 579
should have taken the stairs, and I know I won’t reach my step goal today. You are 580
more conscious of what you do, and sometimes do things that you wouldn’t have done 581
otherwise. (Q9: Medical personnel, performance feedback, low BMI) 582
Moreover, and in contrast to employees with high BMIs, employees with low BMIs related a 583
low performance feedback score to their overall movement, both at work and at home. They 584
expressed the view that a low performance score encouraged them to self-regulate and also 585
move more in the home situation, especially when the work situation lacked opportunities to 586
increase the movement pattern: 587
Well, I was a bit lazy regarding exercising, and now I’m exercising at least once and 588
often twice a week, really consciously. It is a bit dependent of my schedule, and you 589
know, I’m taking the bike more often, and maybe taking longer walks with the dog to 590
move more. (Q10: Medical personnel, performance feedback, low BMI) 591
These differences in compensation behavior between the work and home environment are 592
especially interesting because both employees with high and low BMIs mention that they do 593
regularly exercise in their private time: 594
I usually go to the gym 2 to 3 times a week, depending on my schedule. (Q11: medical 595
personnel, performance feedback, high BMI) 596
I run, about once a week, and once a week I go for a spinning class, and in the 597
weekend when the weather is ok I’m cycling a lot. (Q12: Administrative personnel, 598
performance feedback, medium BMI) 599
Well, we have a dog, so I walk multiple times a day. And I do Pilates, which is good 600
for my body strength, but I can’t really see it in my Fitbit (Q13: Medical personnel, 601
performance feedback, low BMI) 602
Even though their general exercise levels outside of work are comparable, the reasons to alter 603
the amount of exercise are different. 604
4.5.2 BMI, developmental feedback, and Home Health Autonomy
605
In this section, we focus on employees with high BMIs who received developmental 606
feedback, and we aim to shed light on why their perceived autonomy to self-regulate their 607
health in their private time declined, while it remained stable in working hours. 608
15 First, employees with both high and low BMIs that received developmental feedback reported 609
becoming aware of more opportunities to self-regulate their health-related behavior in the 610
workplace: 611
Yes, well, due to that Fitbit, I no longer go to the restaurant to have lunch or dinner, 612
just to not be tempted anymore regarding food. (Q14: Administrative personnel, 613
development feedback, high BMI) 614
Yes, with that Fitbit, well, you see the steps, […] and then I consciously thought, when 615
colleagues were taking the elevator, no, I’ll take the stairs. (Q15: Medical personnel, 616
development feedback, medium BMI) 617
However, employees with high BMIs report negative emotions linked to receiving feedback 618
on their health-related behavior: 619
I recall that at some point we received an e-mail including norm groups [regarding 620
activity levels] […] and then I really felt miserable, because I didn’t fit in those 621
groups. It was great for people who had high step counts, but for people with low step 622
counts that wasn’t nice at all. (Q16: Medical personnel, developmental feedback, high 623
BMI) 624
The advice they received as part of the developmental feedback was aimed at their work 625
situation but, due to its general nature, it could also apply to their private situations, as 626
reported by some employees noting that the ‘health responsibility’ was being shifted from 627
work to home. However, whereas employees with low and medium BMIs commented on this 628
work-home shift in more neutral terms, employees with high BMIs were more negative: 629
Well, when I had to get some groceries, I started to walk. And I’m taking the bicycle 630
more often now, whenever I have to get something in our village. Before, I took the 631
car, but I’m a lot more conscious about that now. (Q17: Medical personnel, 632
development feedback, medium BMI) 633
Well, […] our whole company has to be healthy, and we all have to be good role 634
models. […] And then I start thinking: What’s next? Do I have to lose 20 kilograms of 635
weight, because otherwise I can’t work here? Because I’m not a good role model? 636
(Q18: Medical personnel, developmental feedback, high BMI) 637
This negative labelling of the attention to self-regulation of health-related behavior even in 638
private time was projected onto the fitness opportunities that the employers provided after 639
working hours: these are experienced as stigmatizing by employees with high BMIs. These 640
employees indicate that they sometimes feel they are being watched and judged in their daily 641
job, and feel as if the health programs offered by the employer after working hours are only fit 642
for non-obese colleagues: 643
I know I can join a company fitness class, […] but I’m afraid to do so. Because, who 644
does that? All those trained bodies! I’m not going to stand amidst them, I really won’t. 645
(Q19: Medical personnel, developmental feedback, high BMI) 646
And then they are supporting ‘the week of taking the stairs’ […], but then, when I’m 647
standing in front of the elevator, people tend to say “Oh, are you taking the elevator? 648
We are taking the stairs!”. That feels terrible. Really terrible. (Q20: Medical 649
personnel, developmental feedback, high BMI) 650
16 This supplementary analysis of additional data has provided some insight into the reasons 651
why employees with high BMI respond differently to HSMA feedback than employees with 652
lower BMI. 653
High BMI employees in the performance feedback group attach more salience to the provided 654
norms and standards for healthy behavior, and experience more negative emotions when not 655
reaching the norm, than employees with low BMIs. Further, they report that they increasingly 656
notice limitations that stop them increasing their daily exercise. 657
Under the developmental feedback conditions, we see that both low and high BMI employees 658
see more opportunities to change their workplace behavior, and both are aware that the 659
responsibility for health at work to an extent shifts to the home environment. However, 660
whereas employees with low BMIs comment about this shift in neutral terms, employees with 661
high BMIs see this negatively. Further, the health promotion programs offered by the 662
employer after working hours are frowned upon by those with high BMIs because they feel 663
judged by these programs. 664
5 Discussion
665
5.1 Discussion of the results
666
This study provides several new insights regarding the use of HSMAs in the workplace and 667
their influence on employees’ autonomy to regulate their own health-related behavior. We 668
will first summarize the results of our study, after which we will discuss the theoretical and 669
practical contributions. We also present some limitations and potential directions for future 670
research. 671
This study shows that the use of HSMAs, such as the Fitbit, does not influence employees’ 672
perceived autonomy in self-regulating their health-related behavior at the workplace, i.e. their 673
work health autonomy (WHA), whereas it does reduce this perceived autonomy in the private 674
situation, i.e. home health autonomy (HHA). Looking at the effects of the type of feedback 675
that participants received, we found that adding developmental feedback to performance 676
feedback marginally enhanced the experienced WHA, but had no impact on HHA. Finally, we 677
looked at the impact of using BMI as a single proxy for health status on these results, and we 678
found that the effects of HSMAs on both WHA and HHA were negatively affected by BMI. 679
That is, employees with a higher BMI suffered a greater loss of perceived autonomy in self-680
managing their health. Further, employees with a low BMI who received performance 681
feedback experienced a relatively smaller loss of WHA than those with higher BMIs, and also 682
reported an increase in HHA. The combined effects of feedback focus and BMI showed that 683
the addition of developmental feedback mitigates the negative effects of HSMAs on WHA for 684
employees with high BMIs, but at the same time decreases the HHA for these employees. 685
To better understand the influence of feedback focus and BMI interaction effects, we 686
conducted additional interviews with participants with various BMIs. It showed that 687
employees with high BMIs experienced, for several reasons, relatively less autonomy in self-688
regulating their health-related behavior in both the home and work situation. First, they tend 689
to assign more salience to the general norms provided (i.e. walking 10,000 steps per day) than 690
employees with lower BMIs. Employees with a low BMI experience the norm as a loose 691
guideline, whereas people with a high BMI consider it as an important and strict norm that 692
they are difficult to meet. When employees with high BMI then do not reach this norm, they 693
experience negative emotions, and they express that they become increasingly aware of the 694
limitations imposed by their surroundings that prevent them from reaching the norm. Further, 695
